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Percolation Images: Fractal Geometry Features for Brain Tumor Classification.

Alessandra Lumini1, Guilherme Freire Roberto2, Leandro Alves Neves3

  • 1Department of Computer Science and Engineering, University of Bologna, Cesena, FC, Italy. alessandra.lumini@unibo.it.

Advances in Neurobiology
|March 12, 2024
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Summary

This study introduces a hybrid approach for brain tumor detection using fractal geometry and deep learning. Generating "percolation" images enhances spatial properties, improving tumor classification accuracy in medical imaging.

Keywords:
Brain tumorsClassification ensembleDeep learningFeature representationsFractal features

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Accurate brain tumor detection is vital for effective clinical diagnosis and treatment planning.
  • Traditional methods may not fully capture complex spatial characteristics of tumors.
  • Deep learning models require robust feature extraction for optimal performance.

Purpose of the Study:

  • To develop a hybrid brain tumor classification framework combining fractal geometry and deep learning.
  • To investigate the utility of fractal-based "percolation" images for enhancing tumor detection.
  • To improve the accuracy and efficiency of automated brain tumor diagnosis.

Main Methods:

  • A novel hybrid approach integrating fractal geometry and convolutional neural networks (CNNs).
  • Generation of "percolation" images using fractal geometry concepts to highlight spatial features.
  • Inputting both original and "percolation" images into a CNN for tumor classification.
  • Validation using a widely recognized benchmark dataset for brain tumor imaging.

Main Results:

  • The proposed hybrid method demonstrated enhanced performance in brain tumor classification.
  • "Percolation" images significantly improved the system's ability to detect tumors.
  • Experimental results on a benchmark dataset confirmed the effectiveness of the approach.

Conclusions:

  • The integration of fractal geometry features, specifically "percolation" images, offers a valuable enhancement for deep learning-based brain tumor detection.
  • This hybrid approach shows promise for improving the accuracy of automated medical image analysis in oncology.
  • The findings suggest a potential pathway for more precise and efficient brain tumor diagnosis.